Journal of Industrial Engineering and Management Studies

Abstract Customer churn prediction has been gaining significant attention due to the increasing competition among mobile service providers. Machine learning algorithms are commonly used to predict churn; however, their performance can still be improved due to the complexity of customer data structure. Additionally, the lack of interpretability in their results leads to a lack of trust among managers. In this study, a step-by-step framework consisting of three layers is proposed to predict customer churn with high interpretability. The first layer utilizes data preprocessing techniques, the second layer proposes a novel classification model based on supervised and unsupervised algorithms, and the third layer uses evaluation criteria to improve interpretability. The proposed model outperforms existing models in both predictive and descriptive scores. The novelties of this paper lie in proposing a hybrid machine learning model for customer churn prediction and evaluating its interpretability using extracted indicators. Results demonstrate the superiority of clustered dataset versions of models over non-clustered versions, with KNN achieving a recall score of almost 99% for the first layer and the cluster decision tree achieving a 96% recall score for the second layer. Additionally, parameter sensitivity and stability are found to be effective interpretability evaluation metrics. 

Abstract This paper introduces a Travel Demand Management (TDM) model in order to decrease the transportation externalities by affecting on passengers’travel choices. Thus, a bi-objective bi-modal optimization model for road pricing is developed aiming to enhance environmental and social sustainability by considering to minimize the air pollution and maximize the social welfare as its objectives. This model determines optimal prices (bus fare and car toll) and optimal bus frequency simultaneously in an integrated model. The model is based on discrete choice theory and consideres the modes’ utility functions in its formulation. The proposed model is solved by two meta-heuristic methods (Non-dominated Sorting Genetic Algorithm-II (NSGA-II), Multi-Objectives Harmony Search (MOHS)) and the numerical results of a case study in Tehran are presented. The main managerial insights resulted from this case study is that its results support the idea of “free public transportation” or subsidizing the public transport as an effective way to decrease the transport related air pollution

Abstract The present study attempts to establish a new framework to speculate customer lifetime value by a stochastic approach. In this research the customer lifetime value is considered as combination of customer’s present and future value. At first step of our desired model, it is essential to define customer groups based on their behavior similarities, and in second step a mechanism to count current value, and at the end estimate the future value of customers. Having a structure in modeling customer churn is also important to have complete customer lifetime value computation. Clustering as one of data mining techniques is practiced to help us analyze the different groups of customers, and extract mathematical model to count the customers value. Thereafter by using Markov chain model as stochastic approach, we predict future behavior of the customer and as a result, estimate future value of different customers. The proposed model is demonstrated by the customer demographic data and historical transaction data in a composite manufacturing company in Iran.